About the job
Exciting opportunity for a Principal Architect to lead the development of scalable AI frameworks at Adobe Express. Drive innovation in large-scale distributed systems, ML, and LLM orchestration. Collaborate with top engineers and researchers to shape the future of AI-powered creativity. Join us to build the next generation of AI platforms at scale.
Responsibilities
Architect and evolve the complete AI stack for Adobe Express — covering Agentic AI, Construct AI, Imaging AI, Motion AI, and Personalization AI.
Develop and operationalize end-to-end systems — integrating microservices, data pipelines, LLM orchestration layers, in-house and third-party models, databases, caches, session analytics, and evaluation systems into a cohesive architecture.
Develop large-scale data and inference infrastructure to support model training, fine-tuning, evaluation, and deployment — employing Spark, Kafka, Flink, and other distributed frameworks.
Develop high-performance runtime services for inference and orchestration with strong observability, fault tolerance, and latency guarantees.
Apply strong caching and storage tactics to enhance efficiency and cost-effectiveness for various AI workloads.
Lead development of experimentation and evaluation systems, encompassing session-level analytics, feedback loops, and quality metrics that drive continuous improvement.
Work closely with applied research, product, and platform teams to implement LLMs and other AI models into customer-facing services.
Drive architectural strategy for Express AI Foundations — connecting ML models, reasoning engines, and data streams into adaptive, intelligent systems.
Mentor senior engineers and scientists, encouraging excellence across architecture, experimentation, and AI system composition.
Qualifications
Minimum
10+ years of experience in large-scale distributed systems AI infrastructure, or ML platform engineering.
Deep understanding of ML and LLM fundamentals — training, fine-tuning, deployment, and evaluation workflows.
Proven expertise in building and scaling data pipelines, real-time streaming systems, and event-driven architectures (Kafka, Spark, Flink, etc.).
Strong background in caching strategies, database development, and performance optimization for large-scale serving systems.
Hands-on experience with LLM orchestration frameworks, model routing, and multi-model inference.
Proficiency in Python, Java, C++, or Go, with an emphasis on distributed systems, cloud-native deployment, and performance tuning.
Familiarity with Agentic AI patterns — reasoning loops, memory persistence, task decomposition, and multi-agent coordination.
Skill to merge engineering precision with practical research understanding, connecting prototyping and production.
Strong communication and collaboration skills, with experience influencing cross-functional technical direction.
Preferred
Bachelor's or equivalent experience in Computer Science, Data Science, Machine Learning, or a related technical field.
Experience architecting AI assistants, build agents, or multimodal creative systems.
Exposure to Generative AI (LLMs, diffusion, or multimodal architectures).
Experience with MLOps pipelines, feature stores, and model registries.
Track record of open-source contributions, publications, or conference presentations.